{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\admin\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "home_folder = os.path.expanduser(\"~\")\n",
    "print(home_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ionosphere.data\n"
     ]
    }
   ],
   "source": [
    "# Change this to the location of your dataset\n",
    "data_filename = \"data/ionosphere.data\"\n",
    "data_filename=\"ionosphere.data\"\n",
    "print(data_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "import numpy as np\n",
    "\n",
    "# Size taken from the dataset and is known already\n",
    "X = np.zeros((351, 34), dtype='float')\n",
    "y = np.zeros((351,), dtype='bool')\n",
    "\n",
    "with open(data_filename, 'r') as input_file:\n",
    "    reader = csv.reader(input_file)\n",
    "    for i, row in enumerate(reader):\n",
    "        # Get the data, converting each item to a float\n",
    "        data = [float(datum) for datum in row[:-1]]\n",
    "        # Set the appropriate row in our dataset\n",
    "        X[i] = data\n",
    "        # 1 if the class is 'g', 0 otherwise\n",
    "        y[i] = row[-1] == 'g'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 263 samples in the training dataset\n",
      "There are 88 samples in the testing dataset\n",
      "Each sample has 34 features\n"
     ]
    }
   ],
   "source": [
    "#from sklearn.cross_validation import train_test_split\n",
    "from sklearn.model_selection  import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=14)\n",
    "print(\"There are {} samples in the training dataset\".format(X_train.shape[0]))\n",
    "print(\"There are {} samples in the testing dataset\".format(X_test.shape[0]))\n",
    "print(\"Each sample has {} features\".format(X_train.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "estimator = KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": "KNeighborsClassifier()"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimator.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy is 86.4%\n"
     ]
    }
   ],
   "source": [
    "y_predicted = estimator.predict(X_test)\n",
    "accuracy = np.mean(y_test == y_predicted) * 100\n",
    "print(\"The accuracy is {0:.1f}%\".format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#from sklearn.cross_validation import cross_val_score\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average accuracy is 82.6%\n"
     ]
    }
   ],
   "source": [
    "scores = cross_val_score(estimator, X, y, scoring='accuracy')\n",
    "average_accuracy = np.mean(scores) * 100\n",
    "print(\"The average accuracy is {0:.1f}%\".format(average_accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "avg_scores = []\n",
    "all_scores = []\n",
    "parameter_values = list(range(1, 21))  # Including 20\n",
    "for n_neighbors in parameter_values:\n",
    "    estimator = KNeighborsClassifier(n_neighbors=n_neighbors)\n",
    "    scores = cross_val_score(estimator, X, y, scoring='accuracy')\n",
    "    avg_scores.append(np.mean(scores))\n",
    "    all_scores.append(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Object `plt.plot` not found.\n"
     ]
    }
   ],
   "source": [
    "plt.plot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x150f57f6cd0>]"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 2304x1440 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.figure(figsize=(32,20))\n",
    "plt.plot(parameter_values, avg_scores, '-o', linewidth=5, markersize=24)\n",
    "#plt.axis([0, max(parameter_values), 0, 1.0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for parameter, scores in zip(parameter_values, all_scores):\n",
    "    n_scores = len(scores)\n",
    "    plt.plot([parameter] * n_scores, scores, '-o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x150f590c3a0>,\n <matplotlib.lines.Line2D at 0x150f590c400>,\n <matplotlib.lines.Line2D at 0x150f590c460>,\n <matplotlib.lines.Line2D at 0x150f590c520>,\n <matplotlib.lines.Line2D at 0x150f590c5e0>]"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(parameter_values, all_scores, 'bx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "all_scores = defaultdict(list)\n",
    "parameter_values = list(range(1, 21))  # Including 20\n",
    "for n_neighbors in parameter_values:\n",
    "    for i in range(100):\n",
    "        estimator = KNeighborsClassifier(n_neighbors=n_neighbors)\n",
    "        scores = cross_val_score(estimator, X, y, scoring='accuracy', cv=10)\n",
    "        all_scores[n_neighbors].append(scores)\n",
    "for parameter in parameter_values:\n",
    "    scores = all_scores[parameter]\n",
    "    n_scores = len(scores)\n",
    "    plt.plot([parameter] * n_scores, scores, '-o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x150f6426670>]"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(parameter_values, avg_scores, '-o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_broken = np.array(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_broken[:,::2] /= 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The original average accuracy for is 82.6%\n",
      "The 'broken' average accuracy for is 73.8%\n"
     ]
    }
   ],
   "source": [
    "estimator = KNeighborsClassifier()\n",
    "original_scores = cross_val_score(estimator, X, y,\n",
    "  scoring='accuracy')\n",
    "print(\"The original average accuracy for is {0:.1f}%\".format(np.mean(original_scores) * 100))\n",
    "broken_scores = cross_val_score(estimator, X_broken, y,\n",
    "  scoring='accuracy')\n",
    "print(\"The 'broken' average accuracy for is {0:.1f}%\".format(np.mean(broken_scores) * 100))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_transformed = MinMaxScaler().fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average accuracy for is 82.9%\n"
     ]
    }
   ],
   "source": [
    "X_transformed = MinMaxScaler().fit_transform(X_broken)\n",
    "estimator = KNeighborsClassifier()\n",
    "transformed_scores = cross_val_score(estimator, X_transformed, y, \n",
    "  scoring='accuracy')\n",
    "print(\"The average accuracy for is {0:.1f}%\".format(np.mean(transformed_scores) * 100))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scaling_pipeline = Pipeline([('scale', MinMaxScaler()),\n",
    "                             ('predict', KNeighborsClassifier())])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The pipeline scored an average accuracy for is 82.9%\n"
     ]
    }
   ],
   "source": [
    "scores = cross_val_score(scaling_pipeline, X_broken, y, scoring='accuracy')\n",
    "print(\"The pipeline scored an average accuracy for is {0:.1f}%\".format(np.mean(transformed_scores) * 100))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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